Nagadantyadi Ghrita In Keeta Visha - A Review

Background: Insect bites or stings are very common especially in the rural parts of India. Management of Keeta Visha has been explained in all the Ayurveda classics. Nagadantyadi Ghrita is a formulation indicated in various poisonous conditions by Acharya Charaka. Aim: This study aimed at evaluating the pharmacological properties of Nagadantyai Ghrita and its effect on Keeta Visha. Materials and Methods: This literature study was done by referring various Samhita’s, articles and online sources. Observations and Results: There are 8 ingredients in Nagadantyadi Ghrita and among them all most all ingredients show Vedanasthapana, Vishagna and Shothahara properties. Conclusion: Oral intake of Nagadantyadi Ghrita can manage the symptoms of Keeta Visha like pain, swelling, itching and burning sensation

Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models

Deep learning recommendation models (DLRMs) are used across many business-critical services at Facebook and are the single largest AI application in terms of infrastructure demand in its data-centers. In this paper we discuss the SW/HW co-designed solution for high-performance distributed training of large-scale DLRMs. We introduce a high-performance scalable software stack based on PyTorch and pair it with the new evolution of Zion platform, namely ZionEX. We demonstrate the capability to train very large DLRMs with up to 12 Trillion parameters and show that we can attain 40X speedup in terms of time to solution over previous systems. We achieve this by (i) designing the ZionEX platform with dedicated scale-out network, provisioned with high bandwidth, optimal topology and efficient transport (ii) implementing an optimized PyTorch-based training stack supporting both model and data parallelism (iii) developing sharding algorithms capable of hierarchical partitioning of the embedding tables along row, column dimensions and load balancing them across multiple workers; (iv) adding high-performance core operators while retaining flexibility to support optimizers with fully deterministic updates (v) leveraging reduced precision communications, multi-level memory hierarchy (HBM+DDR+SSD) and pipelining. Furthermore, we develop and briefly comment on distributed data ingestion and other supporting services that are required for the robust and efficient end-to-end training in production environments

Soft-computing-based false alarm reduction for hierarchical data of intrusion detection system

A false alarm rate of online anomaly-based intrusion detection system is a crucial concern. It is challenging to implement in the real-world scenarios when these anomalies occur sporadically. The existing intrusion detection system has been developed to limit or decrease the false alarm rate. However, the state-of-the-art approaches are attack or algorithm specific, which is not generic. In this article, a soft-computing-based approach has been designed to reduce the false-positive rate for hierarchical data of anomaly-based intrusion detection system. The recurrent neural network model is applied to classify the data set of intrusion detection system and normal instances for various subclasses. The designed approach is more practical, reason being, it does not require any assumption or knowledge of the data set structure. Experimental evaluation is conducted on various attacks on KDDCup’99 and NSL-KDD data sets. The proposed method enhances the intrusion detection systems that can work with data with dependent and independent features. Furthermore, this approach is also beneficial for real-life scenarios with a low occurrence of attacks

Soft-computing-based false alarm reduction for hierarchical data of intrusion detection system

A false alarm rate of online anomaly-based intrusion detection system is a crucial concern. It is challenging to implement in the real-world scenarios when these anomalies occur sporadically. The existing intrusion detection system has been developed to limit or decrease the false alarm rate. However, the state-of-the-art approaches are attack or algorithm specific, which is not generic. In this article, a soft-computing-based approach has been designed to reduce the false-positive rate for hierarchical data of anomaly-based intrusion detection system. The recurrent neural network model is applied to classify the data set of intrusion detection system and normal instances for various subclasses. The designed approach is more practical, reason being, it does not require any assumption or knowledge of the data set structure. Experimental evaluation is conducted on various attacks on KDDCup’99 and NSL-KDD data sets. The proposed method enhances the intrusion detection systems that can work with data with dependent and independent features. Furthermore, this approach is also beneficial for real-life scenarios with a low occurrence of attacks. </jats:p

A study of serum phosphate levels and its correlation with curb-65 score in community acquired pneumonia

Background: Community acquired pneumonia (CAP) refers to pneumonia contracted by a person with little or no contact with health care system. Phosphorus is an essential molecule in ATP, playing a central role in energy production. A normal range is 2.5 to 4.5 mg/dl. Phosphate disturbance are noted in patients with pneumonia. Hypophosphatemia, plays a role in impairing chemotaxis, phagocytosis and bactericidal activity of macrophages. Hyperphosphatemia in pneumonia result in hypocalcaemia and pulmonary calcification. Hence study of serum phosphate levels in community acquired pneumonia is of clinical significance.&#x0D; Aims and Objective: The was aimed to estimate the levels of serum phosphate in patients with community acquired pneumonia and to correlate serum phosphate levels with CURB 65 severity score.&#x0D; Materials and Methods: Seventy-five CAP patients admitted as inpatients are included in this study. Chest X ray is obtained in all suspected cases. Serum phosphate levels are determined through blood investigation on Day 1 and Day 3.&#x0D; Results: Serum phosphate levels on Day 1 and Day 3 were significantly associated with CURB65 score. Both high and low phosphate levels were associated with high CURB 65 score. The association between phosphate levels and CURB 65 score was significant in patients who got discharged but not in patients who expired.&#x0D; Conclusions: Present study of serum phosphate levels as biomarkers in CAP showed that both hypophosphatemia and hyperphosphatemia carried poor prognosis which correlated with high CURB65 score.</jats:p